anticipation, complexity and the future roberto poli

Anticipation, Complexity and the Future

Roberto Poli

http://www.projectanticipation.org

Trento


The city of the Council (1545-1563)

Things are obviouslydifferent …

Cardinals – scholars 18 y meeting – 3 d meeting The big split – the beginning of a (partial)

reunification

The agenda


Chart the territory: understand how different sciences and disciplines address the future

This conference shows that the topic is getting burning-hot Almost 400 submissions About 260 presentations Wide disciplinary coverage: from sociology to engineering,

from philosophy to design, from city planning to anthropology, from psychology to architecture, etc

For which purpose? Forthcoming challenges (and opportunities) ask us move

from a primarily reactive science to a primarily anticipatory one

Change from understanding “what is” to “what is to become” And, as a consequence, help addressing the fragmentation

especially of the human and social sciences


A First Understanding of Anticipation

What Next?

A Methodological Caveat

Anticipation Widely different ideas of anticipation So far, no systematic comparison among the various

proposals has ever been tried We literally do not know whether the same idea has been

discovered times and again or entirely different perspectives have been proposed

Many recent papers/books Appadurai, A. (2013). The Future as Cultural Fact Beckert, J. (2013). Capitalism as a System of Expectations Seligman et al (2013). Navigating Into the Future or Driven by the Past Tavory, I., & Eliasoph, N. (2013). Coordinating Futures: Toward a Theory of

Anticipation Poli, R. (2014). Anticipation: A New Thread for the Human and Social

Sciences? Gergen, K. J. (2015). From Mirroring to World-Making: Research as Future

Forming Miller, R. et al (2013-2016), The Discipline of Anticipation …


Anticipation


The explicit consideration of anticipation opens new scientific perspectives. To mention but two apparently opposed and disconnected outcomes, anticipation both rehabilitates the Aristotelian theory of causes (including a version of the final cause) and generates an innovative understanding of complexity

Our understanding of anticipation is still cursory, and the novelty of the perspective may conceal the difficulty implied by this otherwise refreshingly new vision

Nonetheless, anticipation is increasingly at the heart of urgent contemporary debates, from climate change to economic crisis

Some Questions about Anticipation

• When anticipation does occur in behaviour and life?

• What types of anticipation can be distinguished?

• Which properties of our environment change the pertinence of different types of anticipation?

• Which structures and processes are necessary for anticipatory action?

• Which is the behavioral impact of anticipation?

• How can anticipation be modeled?


Our Starting Point


The future is coming back to the research agenda Reorientation of the research agenda from

understanding “what is” to understanding “what is to become”

From primary past-orientation to primary future-orientation

No awareness of Futures Studies (FS) – a field that has been working with the future for more than 60 years

The few that show some awareness of FS, often misunderstand FS and what it is doing

A Problem

Anticipation


Anticipation

Forecast

ForesightProspection

Protension

As a generic term

As a technical term

?

Both acceptations have their merits

Traditional Futures Studies


Futures Studies as Foresight, that is the analytic exploration of possible futures (e.g., via scenarios)

Forecasts (e.g., time series analyses and their extrapolations) are not a proper part of futures studies

Foresight includes the passage from scenarios to action (i.e, elaboration of strategy)

Futures Studies = Foresight

New Futures Studies


Includes Forecasts as a legitimate component of FS (the building of quantitative models)

Separates the two components of foresight into Foresight proper = analytic exploration of possible

futures (aka the building of qualitative models) Anticipation = the use of models (both qualitative

and quantitative) in action (aka the transformation of models into strategy and decision)

No implied

assessment

of valueAnticipation

Foresight

The Three Levels of Futures Studies


Forecast

Forecast


Properly previsive activity Data based Works well with pretty short (econometry) and

very long (climate change) temporal windows Well-developed theoretical base Powerful math Quantitative Past-oriented

A way of conquering the future from the past

Between forecast and foresight


Something never to be forgotten: Trends deflect They may vanish New trends may arise

i.e., there are surprises and novelties

Moreover Unavailable or unreliable data Qualitative and multi-disciplinary issues Poor understanding of the event’s underlying laws and its

conditioning factors When the forecasts can affect the event we are trying to

forecast When the environment changes in novel and surprising ways Exploration of multiple futures Uncertainty

Foresight


Scenarios Explorative (from the present to the future) Normative (from the future to the present)

Non-previsive Extensive practice – little theory Includes some levels of uncertainty and ambiguity Includes efforts for challenging mental models Includes weak signals and wildcards Some (but usually very limited) acceptation of

complexity Future-oriented

Intermezzo: the role of reframing


Learn to see what you usually do not see – collect widely different information (what is now irrelevant, may become relevant tomorrow)

Learn to see things in a different way – Understand your biases

Realize the structural constraints that influence any of us “Knowing” and “seeing” follow two different logics The “end of history” illusion (Education, culture, relationships, power) (Future-self as the self of another person)

For the time being, one exemplification only!

Why is this important?


The opposition between “seeing” and “knowing” “seeing” – Local information prevails over global information “knowing” – Global information prevails over local

information (as far as the case we have considered is concerned – things

are more complex, however)

All mental models are biased Challenging/changing mental models requires dedicated

interventions One of the deep roots of prejudice

I know I should be vegetarian, but that steak looks so tasty … The more we are deeply focussed on a task, the less we see

of our environment

Reframing Recognize our own mental models (including

their biases and prejudices) and find ways to change them

Sometimes, the solution to our problems requires that we learn to see/know things differently

Our “inherited” models (including biases and prejudices) are all derived from the past – even if they have proven so far successful, this does not imply that they will be successful in the future as well, in situations very different from those we are accustomed to

Anticipation The future is far from being a problem of either

extrapolation from trends or exploration of possible futures

Move from a static understanding of the future as something that is there, to a dynamic/processualistic understanding of the future as something that can be generated or consumed by our deeds

The future becomes a problem of modifying and eventually expand our capacity to act

The future as a problem of designing, implementing and testing new futures

Anticipation


An anticipatory behavior is a behavior that ‘uses’ the future in its actual decisional process

To fix ideas, anticipation includes the outcomes from forecast and foresight and uses them for action

Two mandatory components: a forward-looking attitude and the use of the former’s result for action

A weather forecast in itself is not anticipatory in the sense used here

Watching a weather forecast and as a consequence taking an umbrella before going to work is instead an anticipatory behavior

Anticipation


Anticipation’s two components are coherent with Rosen’s definition of anticipation

“An anticipatory system is a system containing a predictive model of itself and/or its environment, which allows it to change state at an instant in accord with the model’s predictions pertaining to a later instant” (Rosen, 2012, pp. 8, 313, originally published in 1985)

New models developed by our forecast and foresight exercises

Old models already embedded in ourselves and our communities/organizations/institutions

THE PROBLEM: How to successfully use newly created models to modify already embedded models Awareness of cognitive and social

constraints/biases Awareness of complexity

A subtle difference


Old M

New M

Anticipation


Why now? (1) – Because of the challenges we are going to face

Why now? (1I) – Because the (human and social) sciences are refocusing on the future

Why do this? (1) – this is about the recognition of the empirical fact of anticipation in all systems – there is an empirical and theoretical challenge to understand how this works. It is also about building our capacity to reflect upon how anticipatory processes are working, and about broadening our repertoire and our sensitivity and reflexivity about this

Why do this? (II) – to challenge the fragmentation of the human and social sciences; to help them to make a more significant contribution to the world (and, as a consequence: to empower citizens and social groups)



What Next?


What Next? Futures Literacy


Typology of Futures Possible – Plausible – Probable – Preferable (Amara

1981) Tomorrow – 6 months – 3 years – 10 years – …

Ways of Using the Future (Miller) Optimization: How to “colonize” the future (e.g.,

planning) Contingency: How to prepare for anticipated surprises Novelty: How to expand perceptions of the present

Social and psychological constraints obstructing our future-generating capacity

Complexity

Uncertainty


People often feel unconfortable with uncertainty – ambiguity – complexity After all, humans like to control things

Ambiguity and complexity reinforce one another – but are very different Ambiguity is more subjective, complexity more

objective Managing uncertainty is about learning to

manage the feelings you have about ambiguity and the knowledge you have about complexity


There are many reasons for discounting the future. One is: “The End of History Illusion” (Science, 4 Jan 2013, vol. 339)

Extensive survey (19,000 people, aged 18-68) “Young people, middle-aged people, and older people all

believed they had changed a lot in the past but would change relatively little in the future.”

“People, it seems, regard the present as a watershed moment at which they have finally become the person they will be for the rest of their lives.” 10 years ago I was very different from the person I am now –

At the end of the next 10 years I will be the same person I am now

The illusion leads “people to overpay for future opportunities to indulge their current preferences.”

Why the future is so easily discounted?

So, What Can Be Done?


The first step, preliminary to any other more nuanced strategy is to allow ourselves to talk about our future

Literally, to give us permission to talk about our future, to insert the future into our present

This preliminary step is far more difficult than it may appear Because it is unusual – there are very few situations in

which one can legitimately talk about her future Because one does not know how to do it (The experience accumulated by FS can be deeply

helpful)

Complexity No accepted framework or even terminology available Complexity of what?

Complexity of natural and social systems (objective complexity)

Complexity of our understanding of natural and social systems (subjective complexity)

Complexity of the models used for understanding natural and social systems (scientific complexity) [a subclass of the former entry]

Complexity of the interactions between natural and social systems and their models

Does complexity unfold in degrees (a system is more or less complex) or is it a type (a system is either simple or complex)? (continuous vs. discrete understanding of complexity)

An understanding of complexity Complex systems result from

networks of multiple interacting causes that cannot be individually distinguished

must be addressed as entire systems, that is they cannot be addressed in a piecemeal way

they are such that small inputs may result in disproportionate effects the problems they present cannot be solved once and for ever, but

require to be systematically managed and typically any intervention merges into new problems as the result of the interventions to deal with them

the relevant systems cannot be controlled – the best one can do is to influence them, learn to “dance with them” (Meadows, 1999)

Complicated systems originate from causes that can be individually distinguished; can be addressed piece-by-piece; for each input to the system there is a proportionate output; the relevant systems can be controlled and the problems they present admit permanent solutions

My position

Simple Systems

Complex Systems

Complicated systems as a subclass of

simple systems

A simple system does not become complex by making it more complicated, even bewilderingly complicated

An organism contains endless mechanisms without being itself a mechanism The Na-K pump within cell membranes governing the

exchange of ions – etc., etc. A complex system may contain endless simple systems

without being itself a simple system If it is true that the difference between simple and

complex is a difference of type, a collection of simple systems does not generate a complex one (a collection of simple systems may generate a complicated system)

A first intuition

Simple system Complex system

Mechanism Organism

Two kinds of system

The two types of system follow different part-whole

logics

P W P W P

W

P

Parts compose the wholeParts compose the whole and

are produced by the whole itself

Two kinds of system Collection of parts and their assemblage (house) The whole and its parts are given in parallel (cat) – differentiation &

reorganization are produced by the system itself

Two forms of constitution from parts to the whole: a system is a collection of interactive

units from the whole to its parts: constraints on interaction, generation

of virtual parts (functions), generation of new types of interaction

A complex system produces the elements it is made of

Systems

If you take apart a house, and put it back together again, you will almost always have a house, even if it is a poor one. If you do that with a cat, you will almost never have a cat

The cat is composed of whole systems that make a whole system. It can only be disassembled as complete wholes – hence organ replacement is possible if you preserve the organ as a whole

Ok, but why is it so?

Forms of constitution


Two forms of constitution from the parts to the whole: a system is a set of

interacting elements (elements plus relations, that is structure)

from the whole to its elements: a system is an entity able to generate its parts (and their relations)

In the former case a system is a structure; in the latter case a system generates (and modifies) its structures – which implies that the system is not limited to its structure. I call ‘function’ this extra component

This generation requires internal models – the system must include a model of itself (and its environment) Impredicative or self-referntial systems

Bateson

Maturana

Varela

Hofstadter

Rosen

Luhmann

Capra

A problem that has been under discussion for more than 50 years…

Causal cycles (Bateson) Heterarchical loops (Capra) Autopoietic systems (Maturana) Self-referential systems (Luhmann) Strange loops (Hofstadter) Impredicative systems (Rosen) …

The Babel of languages …

It is my claim that Rosen went further than

anybody else

Simple and complex Simple systems are

Mainly formal tools, often perfect for didactic purposes Exceedingly rare among real systems, because the conditions

for making possible a simple system are almost never met (Most) real systems are complex When systems are sufficiently isolated from their

environment, or when we can understand them sufficiently well by looking at their parts only (i.e., at their structure) we can treat them as simple

However, it is important to be aware that in so doing we may be dealing with different kinds of system Treating a complex system as if it were simple Enforcing/constraining a system to work within predetermined

boundaries (which nevertheless can be crossed) – what happen when a system crosses those boundaries?

“Complex” vs. “as-if-it-were simple” system Every complex system can be seen as-if-it-were simple

E.g., by “freezing” its dynamics, transformation, or evolution E.g., by considering only some of its degrees of liberty (e.g., by

constraining its behavior, or its environment) In fact, many real (that is, complex) systems can

(reasonably) be treated as simple systems – because most of the time they predictably work within pre-established boundaries Classical exceptions are provided by those systems that are so

dangerous that even a single failure may end in disastrous outcomes

Another exception considers “fast systems”, where no time is available for reacting to failures (feed-forward cycles)

The problem is that this strategy ends in Dismissing what really makes a complex system complex Transforming complexity into complicatedness (i.e., treating a

complex system as if it were a sufficiently complicated system)

Complex systems

treated as if they were

simple tend to generate wicked

problems

Includes “complicat

ed systems”

The two understandings

By default, systems

are simple

Some of them are complicat

ed

Occasionally,

systems are

complex

Wicked systems

are exceedingly rare

By default, systems

are complex

Simple systems

are limiting cases

Remark 1 The traditional, bureaucratic structure adopted

by organizations and institutions (e.g., governments) derives from an understanding of systems that precedes the discovery of complexity

These structures are tailored to addressing ‘complicated’ (in the above-specified sense) – not ‘complex’ – systems: they work as if problems could be addressed individually and in a piecemeal way, with outputs systematically proportionate to relevant inputs, they aim at managing and controlling the underlying systems

Remark 2 While anticipation has been widely studied

within a number of different disciplines – including biology, anthropology, cognitive and social sciences, research into anticipation is deeply fragmented

Moreover, research has not kept pace with social and scientific demand for insights into these practices, their risks and their uses

Remark 3 Things become even more interesting when one

considers systems including models of themselves and their environment. Here is where the issue of ‘anticipatory systems’ arises

By including a model of its environment, the system can ‘anticipate’ its future and may take decisions in the present moment according to the model’s previsions

This is primarily about the recognition of the empirical fact of anticipation – I won’t claim it is a ‘guaranteed’ (rote or mechanical) recipe for success

This possibility opens new avenues for research and the understanding of systems (Rosen, 1991, 2012)



What Next?


When stepping up to the next level?


From forecast to foresight Unavailable or unreliable data Poor understanding of the

event’s underlying laws and its conditioning factors

When the forecasts can affect the event we are trying to forecast

Qualitative and multi-disciplinary aspects

When the environment changes in novel and surprising ways

Exploration of multiple futures From foresight to anticipation

Uncertainty (ambiguity – complexity) prevails

Challenging and reframing mental models

Anticipation

Foresight

Forecast

Beware!


First: Forecast Then: Foresight Then: Anticipation

This understanding does not rely on (a proper understanding of) complexity Anticipation

Foresight

Forecast

The two main situations


Simple (and complicated) cases Past – Future – Present Begin with quantitative analysis

(forecasts) Then step up to qualitative data

(foresight) Then make sense of the present

(anticipation) Complex cases

Present – Future – Past First develop ‘some’ understanding

of the present (anticipation) Then specify its qualitative aspects

(foresight) And only at the end move to

quantitative analyses (forecast) (+ iterations among them)

Anticipation

Foresight

Forecast

Anticipation

Foresight

Forecast

Further steps


If one feels and knows that everything is ok, she has no reason to change

Exploit or even create cognitive and organizational dissonance

“Make things better by making them worse”

Anticipation

Foresight

Forecast

Anticipation

Foresight

Forecast

Anticipation We have seen fragments of the anticipatory perspective

point Anticipation forces a re-evaluation of the very idea of science While the theory of anticipation emerged from within biology,

its scientific exploitation may have dramatic consequences for the human and social sciences

As a matter of fact all human and social sciences have accepted, to varying extents, what is possibly Newton’s most important implicit assumption, what Rosen called the Zeroth Commandment: “Thou shalt not allow the future to affect the present” (Rosen 1991,

49) The Zeroth Commandment implies that all information

comes from the past and no information comes from the future

Future information At a first sight, the expression “information

that is coming from the future” appears implausible, unless one reads the expression “coming from the future” as concerning information conveyed by a model of the system

“Model” here is a shorthand for a variety of situations including “theory”, “idea”, “guess”, “belief”, “hope” and “fear”

All them convey information on the future Models can be explicit or implicit, already

embedded in the system or newly created

Beware An obvious mistake is to think that anticipation is a

feature that we possess because we are such highly complex and wonderfully sophisticated cognitive agents

This is not what the theory of anticipation claims Indeed, the major surprise arising from the theory of

anticipation is that anticipation is a widespread phenomenon present in and characterizing all types of realities

Life in all its varieties is anticipatory: the brain works in an anticipatory way the mind is obviously anticipatory society and its structures are anticipatory even non-living or non-biological systems can be anticipatory

(providing they include models of themselves)

The main claims

Anticipation is a widespread phenomenon present in and characterizing all types of realities

A proper understanding of anticipation requires the adoption of an innovative conceptual framework

The Three Levels of Futures Studies


Type Nature Window Mental model Theory Compl-

exityOrient-ation

Forecast Previsive

(Primarily) Short

LongNo Good No Past-

oriented

ForesightNon

previsive

(Primarily)Inter-mediate

Partly Poor Poor Future-oriented

Anticip-ation

Non previsiv

e

(Primarily)

Inter-mediate

Yes

Presently under develop-

ment

Good Present-oriented

Conclusion The results arising from the sciences (including

cognitive and social sciences) are fallible and incomplete

Nevertheless, they collectively provide a reasonably good grasp of (aspects of) reality

The scientific image of reality will likely improve in the forthcoming decades and centuries

While many different and presently unknowable developments will be needed in the future, the present task is to help making the transition from a primarily reactive to a primarily anticipatory science

anticipation, complexity and the future roberto poli

Documents

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powerpoint anticipation

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futureroberto poli http

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